Description: Random forest is an ensemble of [[Decision Trees]] that improves accuracy and controls overfitting by averaging multiple trees trained on different subsets of data.
Key Points:
- Reduces overfitting compared to individual decision trees.
- Handles large datasets with higher dimensionality.
- Requires more computational resources.
Applications: Financial forecasting, image classification, healthcare diagnostics.